Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/30853
Appears in Collections:Faculty of Health Sciences and Sport Journal Articles
Peer Review Status: Refereed
Title: Development of an Anthropometric Prediction Model for Fat-Free Mass and Muscle Mass in Elite Athletes
Author(s): Sesbreno, Erik
Slater, Gary
Mountjoy, Margo
Galloway, S D
Keywords: Nutrition and Dietetics
Medicine (miscellaneous)
Orthopedics and Sports Medicine
General Medicine
Issue Date: Mar-2020
Citation: Sesbreno E, Slater G, Mountjoy M & Galloway SD (2020) Development of an Anthropometric Prediction Model for Fat-Free Mass and Muscle Mass in Elite Athletes. International Journal of Sport Nutrition and Exercise Metabolism, 30 (2), pp. 174-181. https://doi.org/10.1123/ijsnem.2019-0232
Abstract: The monitoring of body composition is common in sports given the association with performance. Surface anthropometry is often preferred when monitoring changes for its convenience, practicality, and portability. However, anthropometry does not provide valid estimates of absolute lean tissue in elite athletes. The aim of this investigation was to develop anthropometric models for estimating fat-free mass (FFM) and skeletal muscle mass (SMM) using an accepted reference physique assessment technique. Sixty-four athletes across 18 sports underwent surface anthropometry and dual-energy X-ray absorptiometry (DXA) assessment. Anthropometric models for estimating FFM and SMM were developed using forward selection multiple linear regression analysis and contrasted against previously developed equations. Most anthropometric models under review performed poorly compared with DXA. However, models derived from athletic populations such as the Withers equation demonstrated a stronger correlation with DXA estimates of FFM (r = .98). Equations that incorporated skinfolds with limb girths were more effective at explaining the variance in DXA estimates of lean tissue (Sesbreno FFM [R2 = .94] and Lee SMM [R2 = .94] models). The Sesbreno equation could be useful for estimating absolute indices of lean tissue across a range of physiques if an accepted option like DXA is inaccessible. Future work should explore the validity of the Sesbreno model across a broader range of physiques common to athletic populations.
DOI Link: 10.1123/ijsnem.2019-0232
Rights: Accepted author manuscript version reprinted, by permission, from International Journal of Sport Nutrition and Exercise Metabolism, 2020, 30 (2): 174-181, https://doi.org/10.1123/ijsnem.2019-0232. © Human Kinetics, Inc.
Licence URL(s): https://storre.stir.ac.uk/STORREEndUserLicence.pdf

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